Annotation¶

Dendritic and macrophage spleenic subsets. Progenitor

Lewis et al., 2011

In [1]:
library_load <- suppressMessages(
    
    list(
        
        # Seurat 
        library(Seurat), 
        library(monocle3), 
        
        # SingleR
        library(SingleR), 
        library(SingleCellExperiment), 
        
        # Data 
        library(tidyverse)
        
    )
)
In [2]:
random_seed <- 42
set.seed(random_seed)
In [3]:
options(warn=-1)
In [4]:
# Set working directory to project root
setwd("/research/peer/fdeckert/FD20200109SPLENO")
In [5]:
# Source files
source("plotting_global.R")
source("bin/SeuratQC.R")
source("bin/plotDEA.R")
source("../scFacility/script/dirFacility.R")
In [6]:
# Plotting Theme
ggplot2::theme_set(theme_global_set()) # From project global source()

Parameter settings¶

In [7]:
so_file <- "data/object/so_sct_int_hvg8000.rds"
so_pp_file <- "data/object/so_pp.rds"

Import Seurat object¶

In [8]:
so <- readRDS(so_file)

UMAP¶

In [9]:
DefaultAssay(so) <- "integrated"
so <- FindNeighbors(so, dims=1:10, k.param=50, verbose=FALSE)
so <- FindClusters(so, verbose=FALSE, resolution=0.5, algorithm=1, group.singletons=TRUE)
so <- RunUMAP(so, dims=1:15, n.neighbors=80, verbose=FALSE)
In [10]:
options(repr.plot.width=22.5, repr.plot.height=20)

dplot_1 <- dplot(so, reduction="umap", group_by="seurat_clusters", label=TRUE) + ggtitle("Louvain")
dplot_2 <- dplot(so, reduction="umap", group_by="tissue") + scale_color_manual(values=color$tissue) + ggtitle("Tissue")
dplot_3 <- dplot(so, reduction="umap", group_by="cc_phase_class") + scale_color_manual(values=color$cc_phase_class) + ggtitle("Cell cycle")
dplot_4 <- dplot(so, reduction="umap", group_by="label_fine_haemosphere") + scale_color_manual(values=color$label_fine_haemosphere) + ggtitle("Haemosphere label")
dplot_5 <- dplot(so, reduction="umap", group_by="sample_rep") + ggtitle("Replicate")
dplot_6 <- dplot(so, reduction="umap", group_by="treatment") + scale_color_manual(values=color$treatment) + ggtitle("Treatment")
fplot_1 <- fplot(so, reduction="umap", features="pMt_RNA") + ggtitle("Percentage Mt")
fplot_2 <- fplot(so, reduction="umap", features="pHb_RNA") + ggtitle("Percentage Hb")
fplot_3 <- fplot(so, reduction="umap", features="pRb_RNA") + ggtitle("Percentage Rb")
fplot_4 <- fplot(so, reduction="umap", features="nCount_RNA") + ggtitle("UMI count")
fplot_5 <- fplot(so, reduction="umap", features="nFeature_RNA") + ggtitle("Feature count")

dplot_comb <- dplot_1 + dplot_2 + dplot_3 + dplot_4 + dplot_5 + dplot_6 + fplot_1 + fplot_2 + fplot_3 + fplot_4 + fplot_5 +  plot_layout(ncol=3)
dplot_comb
In [11]:
options(repr.plot.width=22.5, repr.plot.height=3)
DimPlot(so, reduction="umap", group.by="seurat_clusters", split.by="seurat_clusters", label=TRUE) + theme(legend.position="none")
In [12]:
so@meta.data %>% dplyr::group_by(seurat_clusters, treatment, sample_rep) %>% 
    dplyr::summarise(n=n()) %>% data.frame() %>% 
    tidyr::spread(seurat_clusters, n) %>% 
    kableExtra::kable("html") %>% as.character() %>% IRdisplay::display_html()

`summarise()` has grouped output by 'seurat_clusters', 'treatment'. You can
override using the `.groups` argument.
treatment sample_rep 0 1 2 3 4 5 6 7 8 9 10 11 12 13
CpG Rep1 1066 1052 239 173 523 415 516 227 264 166 192 110 109 70
CpG Rep2 1248 1468 600 236 181 798 149 491 224 378 282 93 61 158
NaCl Rep1 275 145 187 406 505 64 554 115 319 115 12 67 101 40
NaCl Rep2 464 210 852 972 287 218 126 503 139 261 121 66 61 58

Lineage gene modules¶

Marker genes are taken from Dahlin et al., 2018

In [13]:
DefaultAssay(so) <- "RNA"
In [14]:
so <- AddModuleScore(so, features=c("Procr"), name="msHSC") #Procr, Fgd5, Hoxb5
so <- AddModuleScore(so, features=c("Dntt"), name="msLy") #Dntt, Flt3, Rag2
so <- AddModuleScore(so, features=c("Cd3g", "Cd3e", "Cd3d", "Cd247"), name="msTly") #Dntt, Flt3, Rag2
so <- AddModuleScore(so, features=c("Igha"), name="msBly") #Dntt, Flt3, Rag2
so <- AddModuleScore(so, features=c("Pf4", "Itga2b", "Gp1bb"), name="msMeg") #Pf4, Itga2b, Gp1bb
so <- AddModuleScore(so, features=c("Klf1", "Epor", "Gata1"), name="msEry") #Klf1, Epor, Gata1
so <- AddModuleScore(so, features=c("Elane", "Gfi1", "Cebpe"), name="msNeu") #Elane, Gfi1, Cebpe
so <- AddModuleScore(so, features=c("Csf1r", "Ly86"), name="msMo") #Irgf8, Csf1r, Ly86
so <- AddModuleScore(so, features=c("Prg2", "Prg3"), name="msEo") #Prg2, Prg3
so <- AddModuleScore(so, features=c("Gzmb", "Cma1"), name="msMa") #Gzmb, Cma1
so <- AddModuleScore(so, features=c("Prss34", "Mcpt8"), name="msBa") #Prss34, Mcpt8
In [15]:
options(repr.plot.width=22.5, repr.plot.height=20)

fplot_1 <- fplot(so, reduction="umap", features="msHSC1") + ggtitle("HSC\n (Procr)") + theme(plot.title=element_text(hjust=0.5))
fplot_2 <- fplot(so, reduction="umap", features="msLy1") + ggtitle("Lymphoid\n (Dntt)") + theme(plot.title=element_text(hjust=0.5))
fplot_3 <- fplot(so, reduction="umap", features="msTly1") + ggtitle("T Lymphoid\n (Cd3, Cd247)") + theme(plot.title=element_text(hjust=0.5))
fplot_4 <- fplot(so, reduction="umap", features="msBly1") + ggtitle("B Lymphoid\n (Igha)") + theme(plot.title=element_text(hjust=0.5))
fplot_5 <- fplot(so, reduction="umap", features="msMeg1") + ggtitle("Megakaryocyte\n (Pf4, Itga2b, Gp1bb)") + theme(plot.title=element_text(hjust=0.5))
fplot_6 <- fplot(so, reduction="umap", features="msEry1") + ggtitle("Erythroid\n (Klf1, Epor, Gata1)") + theme(plot.title=element_text(hjust=0.5))
fplot_7 <- fplot(so, reduction="umap", features="msNeu1") + ggtitle("Neutrophil\n (Elane, Gfi1, Cebpe)") + theme(plot.title=element_text(hjust=0.5))
fplot_8 <- fplot(so, reduction="umap", features="msMo1") + ggtitle("Monocyte\n (Csf1r, Ly86)") + theme(plot.title=element_text(hjust=0.5))
fplot_9 <- fplot(so, reduction="umap", features="msEo1") + ggtitle("Eosinophil\n (Prg2, Prg3)") + theme(plot.title=element_text(hjust=0.5))
fplot_10 <- fplot(so, reduction="umap", features="msMa1") + ggtitle("Mast\n (Gzmb, Cma1)") + theme(plot.title=element_text(hjust=0.5))
fplot_11 <- fplot(so, reduction="umap", features="msBa1") + ggtitle("Basophil\n (Prss34, Mcpt8)") + theme(plot.title=element_text(hjust=0.5))
fplot_12 <- fplot(so, reduction="umap", features="Ms4a3") + ggtitle("GMP\n (Ms4a3)") + theme(plot.title=element_text(hjust=0.5))

dplot_comb <- fplot_1 + fplot_2 + fplot_3 + fplot_4 + fplot_5 + fplot_6 + fplot_7 + fplot_8 + fplot_9 + fplot_10 + fplot_11 + fplot_12 + plot_layout(ncol=4)
dplot_comb

Erythroid marker¶

In [16]:
options(repr.plot.width=22.5, repr.plot.height=10)

fplot_1 <- fplot(so, reduction="umap", features="Gata2") + ggtitle("Gata2")
fplot_2 <- fplot(so, reduction="umap", features="Gata1") + ggtitle("Gata1")
fplot_3 <- fplot(so, reduction="umap", features="Epor") + ggtitle("Epor")
fplot_4 <- fplot(so, reduction="umap", features="Epop") + ggtitle("Epop")
fplot_4 <- fplot(so, reduction="umap", features="Tfrc") + ggtitle("Tfrc")
fplot_5 <- fplot(so, reduction="umap", features="Tfr2") + ggtitle("Tfr2")
fplot_6 <- fplot(so, reduction="umap", features="Icam4") + ggtitle("Icam4")

dplot_comb <- fplot_1 + fplot_2 + fplot_3 + fplot_4 + fplot_5 + fplot_6 + plot_layout(ncol=3)
dplot_comb

Myeloid marker genes¶

In [17]:
options(repr.plot.width=22.5, repr.plot.height=20)

fplot_1 <- fplot(so, reduction="umap", features="Cd8a") + ggtitle("Cd8a")
fplot_2 <- fplot(so, reduction="umap", features="Cd4") + ggtitle("Cd4")
fplot_3 <- fplot(so, reduction="umap", features="H2-Eb1") + ggtitle("H2-Eb1 (MHC II)")
fplot_4 <- fplot(so, reduction="umap", features="Ms4a3") + ggtitle("Ms4a3") # GMP
fplot_5 <- fplot(so, reduction="umap", features="Cx3cr1") + ggtitle("Cx3cr1") # Macrophage DC progenitor (MDP) derived 
fplot_6 <- fplot(so, reduction="umap", features="Csf1r") + ggtitle("Csf1r") # Macrophage DC progenitor (MDP) derived 
fplot_7 <- fplot(so, reduction="umap", features="Irf8") + ggtitle("Irf8") # Macrophage specification 
fplot_8 <- fplot(so, reduction="umap", features="Nr1h3") + ggtitle("Nr1h3") # Macrophage specification 
fplot_9 <- fplot(so, reduction="umap", features="Spic") + ggtitle("Spic") # Macrophage specification 
fplot_10 <- fplot(so, reduction="umap", features="Sell") + ggtitle("CD62L") # Macrophage specification 
fplot_11 <- fplot(so, reduction="umap", features="Esam") + ggtitle("Esam") 
fplot_12 <- fplot(so, reduction="umap", features="Itgam") + ggtitle("Itgam (CD11b)") #CD11b

dplot_comb <- fplot_1 + fplot_2 + fplot_3 + fplot_4 + fplot_5 + fplot_6 + fplot_7 + fplot_8 + fplot_9 + fplot_10 + fplot_11 + fplot_12 + plot_layout(ncol=4)
dplot_comb

Differential expression analysis (DEA)¶

Wilcox cell type marker¶

In [18]:
# dea <- lapply(sort(unique(so$seurat_clusters)), function(seurat_clusters) {FindConservedMarkers(so, ident.1=seurat_clusters, grouping.var="treatment", assay="RNA", slot="data", test.use="wilcox")})
# saveRDS(dea, "result/annotation/dea_leiden_wilcox.rds")
dea <- readRDS("result/annotation/dea_leiden_wilcox.rds")
In [19]:
options(repr.plot.width=25, repr.plot.height=50)

names(dea) <- paste("Cluster", 1:length(dea)-1)
cmdp <- lapply(names(dea), function(title) {conserved_markers_dp(dea[[title]], title=title, n_top=50, filter="pos_only")})

wrap_plots(cmdp, ncol=5)

Loading required package: magrittr


Attaching package: ‘magrittr’


The following object is masked from ‘package:purrr’:

    set_names


The following object is masked from ‘package:tidyr’:

    extract

Update cluster annotation¶

In [20]:
cluster_to_cell_type <- data.frame(
    
    seurat_clusters=c(
        
        "0", 
        "1", 
        "2", 
        "3", 
        "4", 
        "5", 
        "6", 
        "7", 
        "8", 
        "9", 
        "10", 
        "11", 
        "12", 
        "13"
        
    ),
    
    cell_type=c(
        
        "Ery (5)", 
        "Ery (4)", 
        "Ery (1)", 
        "MEP", 
        "MDP (1)", 
        "Ery (3)", 
        "DC CD8-", 
        "Ery (2)", 
        "MDP (2)", 
        "DC CD8+", 
        "MDP (3)", 
        "GMP (2)", 
        "RPM", 
        "GMP (1)"
        
    )
)
In [21]:
cluster_to_cell_type <- dplyr::left_join(so@meta.data, cluster_to_cell_type, by="seurat_clusters") %>% 
    dplyr::mutate(cell_id=rownames(so@meta.data)) %>% 
    tibble::column_to_rownames("cell_id")  %>% 
    dplyr::select(cell_type)
In [22]:
so <- AddMetaData(so, cluster_to_cell_type)
In [23]:
options(repr.plot.width=20, repr.plot.height=5)

dplot_1 <- dplot(so, reduction="umap", group_by="seurat_clusters", label=TRUE) + ggtitle("Leiden")
dplot_2 <- dplot(so, reduction="umap", group_by="cell_type") # + scale_color_manual(values=color$leiden_annotation) + ggtitle("Annotation")

dplot_comb <- dplot_1 + dplot_2 + plot_layout(ncol=2)
dplot_comb

Monocel on all data¶

In [24]:
expression_data <- GetAssayData(so, assay="RNA", slot="counts")
cell_metadata <- so@meta.data
cell_metadata$sample_name <- NULL

cds <- new_cell_data_set(expression_data=expression_data , cell_metadata=cell_metadata)

# Set gene names for plotting
rowData(cds)$gene_name <- rownames(cds)
rowData(cds)$gene_short_name <- rowData(cds)$gene_name
In [25]:
options(repr.plot.width=5, repr.plot.height=5)

cds <- preprocess_cds(cds, num_dim=100)
cds <- align_cds(cds, alignment_group="sample_group", alignment_k=20, residual_model_formula_str="~ nCount_RNA + pMt_RNA")

Aligning cells from different batches using Batchelor. 
Please remember to cite:
	 Haghverdi L, Lun ATL, Morgan MD, Marioni JC (2018). 'Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors.' Nat. Biotechnol., 36(5), 421-427. doi: 10.1038/nbt.4091
In [26]:
options(repr.plot.width=25, repr.plot.height=15)

cds <- reduce_dimension(cds, reduction_method="UMAP", preprocess_method='Aligned', umap.n_neighbors=20)
cds <- cluster_cells(cds, cluster_method="leiden", k=20)

dplot_1 <- plot_cells(cds, label_groups_by_cluster=FALSE,  color_cells_by="treatment", group_label_size=5, show_trajectory_graph=FALSE) + ggtitle("Treatment")
dplot_2 <- plot_cells(cds, label_groups_by_cluster=FALSE,  color_cells_by="cc_phase_class", group_label_size=5, show_trajectory_graph=FALSE) + ggtitle("cc_phase_class")
dplot_3 <- plot_cells(cds, label_groups_by_cluster=FALSE,  color_cells_by="cell_type", group_label_size=5, show_trajectory_graph=FALSE) + ggtitle("Cell type (Seurat)")
dplot_4 <- plot_cells(cds, label_groups_by_cluster=FALSE,  color_cells_by="cluster", group_label_size=5, show_trajectory_graph=FALSE) + ggtitle("Monocle cluster")
dplot_5 <- plot_cells(cds, label_groups_by_cluster=FALSE,  color_cells_by="partition", group_label_size=5, show_trajectory_graph=FALSE) + ggtitle("Partition")

dplot_6 <- plot_cells(cds, label_groups_by_cluster=FALSE,  genes="Gata2", scale_to_range=FALSE, show_trajectory_graph=FALSE) + theme(aspect.ratio=1, legend.position="none")
dplot_7 <- plot_cells(cds, label_groups_by_cluster=FALSE,  genes="Gata1", scale_to_range=FALSE, show_trajectory_graph=FALSE) + theme(aspect.ratio=1, legend.position="none")
dplot_8 <- plot_cells(cds, label_groups_by_cluster=FALSE,  genes="Epor", scale_to_range=FALSE, show_trajectory_graph=FALSE) + theme(aspect.ratio=1, legend.position="none")
dplot_9 <- plot_cells(cds, label_groups_by_cluster=FALSE,  genes="Tfrc", scale_to_range=FALSE, show_trajectory_graph=FALSE) + theme(aspect.ratio=1, legend.position="none")
dplot_10 <- plot_cells(cds, label_groups_by_cluster=FALSE,  genes="Icam4", scale_to_range=FALSE, show_trajectory_graph=FALSE) + theme(aspect.ratio=1, legend.position="none")

dplot_11 <- plot_cells(cds, label_groups_by_cluster=FALSE,  genes="Cd4", scale_to_range=FALSE, show_trajectory_graph=FALSE) + theme(aspect.ratio=1, legend.position="none")
dplot_12 <- plot_cells(cds, label_groups_by_cluster=FALSE,  genes="Cd8a", scale_to_range=FALSE, show_trajectory_graph=FALSE) + theme(aspect.ratio=1, legend.position="none")
dplot_13 <- plot_cells(cds, label_groups_by_cluster=FALSE,  genes="Itgam", scale_to_range=FALSE, show_trajectory_graph=FALSE) + theme(aspect.ratio=1, legend.position="none")
dplot_14 <- plot_cells(cds, label_groups_by_cluster=FALSE,  genes="Esam", scale_to_range=FALSE, show_trajectory_graph=FALSE) + theme(aspect.ratio=1, legend.position="none")
dplot_15 <- plot_cells(cds, label_groups_by_cluster=FALSE,  genes="Foxp3", scale_to_range=FALSE, show_trajectory_graph=FALSE) + theme(aspect.ratio=1, legend.position="none")

dplot_1 + dplot_2 + dplot_3 + dplot_4 + dplot_5 + dplot_6 + dplot_7 + dplot_8 + dplot_9 + dplot_10 + dplot_11 + dplot_12 + dplot_13 + dplot_14 + dplot_15 + plot_layout(ncol=5)
In [27]:
cds <- learn_graph(cds)

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In [28]:
options(repr.plot.width=25, repr.plot.height=5)

dplot_1 <- plot_cells(cds, label_groups_by_cluster=FALSE,  color_cells_by="treatment", group_label_size=5) + ggtitle("Treatment")
dplot_2 <- plot_cells(cds, label_groups_by_cluster=FALSE,  color_cells_by="cc_phase_class", group_label_size=5) + ggtitle("cc_phase_class")
dplot_3 <- plot_cells(cds, label_groups_by_cluster=FALSE,  color_cells_by="cell_type", group_label_size=5) + ggtitle("Cell type (Seurat)")
dplot_4 <- plot_cells(cds, label_groups_by_cluster=FALSE,  color_cells_by="cluster", group_label_size=5) + ggtitle("Monocle cluster")
dplot_5 <- plot_cells(cds, label_groups_by_cluster=FALSE,  color_cells_by="partition", group_label_size=5) + ggtitle("Partition")

dplot_1 + dplot_2 + dplot_3 + dplot_4 + dplot_5 + plot_layout(ncol=5)

Recluster Monocytes/DC/Macrophages with Monocle3 and subcluster¶

In [ ]:

Save result¶

In [29]:
saveRDS(so, so_pp_file)

Session info¶

In [30]:
sessionInfo()

R version 4.1.0 (2021-05-18)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux 8.2 (Ootpa)

Matrix products: default
BLAS/LAPACK: /home/fdeckert/bin/miniconda3/envs/r.4.1.0-FD20200109SPLENO/lib/libopenblasp-r0.3.15.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] magrittr_2.0.3              Matrix_1.4-1               
 [3] patchwork_1.1.1             RColorBrewer_1.1-3         
 [5] forcats_0.5.1               stringr_1.4.0              
 [7] dplyr_1.0.9                 purrr_0.3.4                
 [9] readr_2.1.2                 tidyr_1.2.0                
[11] tibble_3.1.7                ggplot2_3.3.6              
[13] tidyverse_1.3.1             SingleR_1.8.1              
[15] monocle3_1.0.0              SingleCellExperiment_1.16.0
[17] SummarizedExperiment_1.24.0 GenomicRanges_1.46.1       
[19] GenomeInfoDb_1.30.1         IRanges_2.28.0             
[21] S4Vectors_0.32.4            MatrixGenerics_1.6.0       
[23] matrixStats_0.62.0          Biobase_2.54.0             
[25] BiocGenerics_0.40.0         sp_1.4-7                   
[27] SeuratObject_4.1.0          Seurat_4.1.1               

loaded via a namespace (and not attached):
  [1] utf8_1.2.2                reticulate_1.24          
  [3] tidyselect_1.1.2          htmlwidgets_1.5.4        
  [5] grid_4.1.0                BiocParallel_1.28.3      
  [7] Rtsne_0.16                munsell_0.5.0            
  [9] ScaledMatrix_1.2.0        codetools_0.2-18         
 [11] ica_1.0-2                 pbdZMQ_0.3-7             
 [13] future_1.25.0             miniUI_0.1.1.1           
 [15] batchelor_1.10.0          withr_2.5.0              
 [17] spatstat.random_2.2-0     colorspace_2.0-3         
 [19] progressr_0.10.0          highr_0.9                
 [21] knitr_1.39                uuid_1.1-0               
 [23] rstudioapi_0.13           ROCR_1.0-11              
 [25] tensor_1.5                listenv_0.8.0            
 [27] labeling_0.4.2            repr_1.1.4               
 [29] GenomeInfoDbData_1.2.7    polyclip_1.10-0          
 [31] farver_2.1.0              parallelly_1.31.1        
 [33] vctrs_0.4.1               generics_0.1.2           
 [35] xfun_0.30                 R6_2.5.1                 
 [37] rsvd_1.0.5                bitops_1.0-7             
 [39] spatstat.utils_2.3-0      DelayedArray_0.20.0      
 [41] assertthat_0.2.1          promises_1.2.0.1         
 [43] scales_1.2.0              rgeos_0.5-9              
 [45] gtable_0.3.0              beachmat_2.10.0          
 [47] Cairo_1.5-15              globals_0.14.0           
 [49] goftest_1.2-3             rlang_1.0.2              
 [51] systemfonts_1.0.4         splines_4.1.0            
 [53] lazyeval_0.2.2            spatstat.geom_2.4-0      
 [55] broom_0.8.0               reshape2_1.4.4           
 [57] abind_1.4-5               modelr_0.1.8             
 [59] backports_1.4.1           httpuv_1.6.5             
 [61] tools_4.1.0               kableExtra_1.3.4         
 [63] ellipsis_0.3.2            spatstat.core_2.4-2      
 [65] proxy_0.4-26              ggridges_0.5.3           
 [67] Rcpp_1.0.8.3              plyr_1.8.7               
 [69] base64enc_0.1-3           sparseMatrixStats_1.6.0  
 [71] zlibbioc_1.40.0           RCurl_1.98-1.6           
 [73] rpart_4.1.16              deldir_1.0-6             
 [75] pbapply_1.5-0             viridis_0.6.2            
 [77] cowplot_1.1.1             zoo_1.8-10               
 [79] haven_2.5.0               ggrepel_0.9.1            
 [81] cluster_2.1.3             fs_1.5.2                 
 [83] data.table_1.14.2         RSpectra_0.16-1          
 [85] scattermore_0.8           ResidualMatrix_1.4.0     
 [87] lmtest_0.9-40             reprex_2.0.1             
 [89] RANN_2.6.1                fitdistrplus_1.1-8       
 [91] hms_1.1.1                 mime_0.12                
 [93] evaluate_0.15             xtable_1.8-4             
 [95] readxl_1.4.0              gridExtra_2.3            
 [97] compiler_4.1.0            KernSmooth_2.23-20       
 [99] crayon_1.5.1              htmltools_0.5.2          
[101] mgcv_1.8-40               later_1.3.0              
[103] tzdb_0.3.0                lubridate_1.8.0          
[105] DBI_1.1.2                 dbplyr_2.1.1             
[107] MASS_7.3-57               leidenbase_0.1.11        
[109] cli_3.3.0                 parallel_4.1.0           
[111] igraph_1.3.1              pkgconfig_2.0.3          
[113] scuttle_1.4.0             IRdisplay_1.1            
[115] plotly_4.10.0             spatstat.sparse_2.1-1    
[117] xml2_1.3.3                svglite_2.1.0            
[119] webshot_0.5.3             XVector_0.34.0           
[121] rvest_1.0.2               digest_0.6.29            
[123] sctransform_0.3.3         RcppAnnoy_0.0.19         
[125] spatstat.data_2.2-0       rmarkdown_2.14           
[127] cellranger_1.1.0          leiden_0.3.10            
[129] uwot_0.1.11               DelayedMatrixStats_1.16.0
[131] shiny_1.7.1               lifecycle_1.0.1          
[133] nlme_3.1-157              jsonlite_1.8.0           
[135] BiocNeighbors_1.12.0      limma_3.50.3             
[137] viridisLite_0.4.0         fansi_1.0.3              
[139] pillar_1.7.0              lattice_0.20-45          
[141] fastmap_1.1.0             httr_1.4.3               
[143] survival_3.3-1            glue_1.6.2               
[145] png_0.1-7                 stringi_1.7.6            
[147] BiocSingular_1.10.0       IRkernel_1.3             
[149] irlba_2.3.5               future.apply_1.9.0